Decision_Tree_Regressor and Classification Problem Method-50_Startup_Company_practice.ipynb

  1. Data Preprocessing
  2. Split the data into dependent and independent variable
  3. splitting the data into training and test
  4. Building Decision Tree Regressor Model
  5. !image.png
  6. Predict Test Data with Decision Tree Regressor Model
  7. Evaluate your Model
  8. Since I am getting 100% in training accuracy so this is an overfitting variance is okay but 100 % I am getting training accuracy - K-Fold
  9. Using Cross Validation Method - K fold Method
  10. linear Regression Model
  11. Random Forest Regressor Model
  12. Classification Problem
  13. Data Preprocessing
  14. Encoding
  15. Outlier
  16. Feature Scaling
  17. Imbalance Treatment
  18. Multivariate Analysis
  19. To understand if Else condition of DT
  20. Imbalance Treatment
  21. Do UnderSampling in Text Analytics where we have huge dataset
  22. Always do oversampling
  23. Building Decision Tree Model
  24. Predict the test data by using both the models
  25. Evaluate Your Model
  26. Cross Validation - K_Fold_Method
  27. Post Pruning
  28. Using HypeParamete in DT to apply post pruning Method
  29. User Definded Functions
  30. Logistic Regression
  31. Bagging Classifier
  32. Random Forest Classifier
  33. Applying Cross Validation Method
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Decision_Tree_Regressor and Classification Problem Method-50_Startup_Company_practice

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[1]:
import os
os.getcwd()
[1]:
'C:\\Users\\RAHUL SRIVASTAVA\\Desktop\\Learnbay\\ML\\Decision_Tree'
[46]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
[47]:
dataset = pd.read_csv('50_Startups.csv')
dataset.head()
[47]:
R&D Spend Administration Marketing Spend State Profit
0 165349.20 136897.80 471784.10 New York 192261.83
1 162597.70 151377.59 443898.53 California 191792.06
2 153441.51 101145.55 407934.54 Florida 191050.39
3 144372.41 118671.85 383199.62 New York 182901.99
4 142107.34 91391.77 366168.42 Florida 166187.94
# Data Preprocessing

Data Preprocessing¶

[48]:
# 1. Handling Missing Value
# 2. Handling Outlier - Outlier only require when wehave large tree to avoid overfitting
# Overfitting means your model is not pure it is impure means there is lots of error is coming.Thatswhy you are getting overfitting
# 3. Encoding - In tree based model always use label encoding method
# 4. Feature Scaling - Please note,there is no need to do feature scaling in DT bcoz we are just allocating whether that value will represent or not.
# 5. Imbalance Treatment
[49]:
dataset.isnull().sum()
[49]:
R&D Spend          0
Administration     0
Marketing Spend    0
State              0
Profit             0
dtype: int64
[50]:
dataset.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 5 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   R&D Spend        50 non-null     float64
 1   Administration   50 non-null     float64
 2   Marketing Spend  50 non-null     float64
 3   State            50 non-null     object 
 4   Profit           50 non-null     float64
dtypes: float64(4), object(1)
memory usage: 2.1+ KB
[51]:
dataset['State'] = dataset['State'].astype('category')
dataset['State'] = dataset['State'].cat.codes
[52]:
dataset.head()
[52]:
R&D Spend Administration Marketing Spend State Profit
0 165349.20 136897.80 471784.10 2 192261.83
1 162597.70 151377.59 443898.53 0 191792.06
2 153441.51 101145.55 407934.54 1 191050.39
3 144372.41 118671.85 383199.62 2 182901.99
4 142107.34 91391.77 366168.42 1 166187.94
# Split the data into dependent and independent variable

Split the data into dependent and independent variable¶

[53]:
x = dataset.iloc[:,0:-1]
y = dataset[['Profit']]
[54]:
x.shape,y.shape
[54]:
((50, 4), (50, 1))
# splitting the data into training and test

splitting the data into training and test¶

[63]:
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=1)
# Building Decision Tree Regressor Model

[64]:
from sklearn.tree import DecisionTreeRegressor
dtRegressor = DecisionTreeRegressor()
dtRegressor.fit(x_train,y_train)
[64]:
DecisionTreeRegressor()
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DecisionTreeRegressor()
[65]:
# Decision Tree is non Parametric model here we don't have any Assumptions like Linear Regression
# ![image.png](attachment:1768890f-9e97-464a-9855-231eb7c51d3a.png)

image.png¶

# Predict Test Data with Decision Tree Regressor Model
[66]:
y_pred_train = dtRegressor.predict(x_train)
y_pred_test = dtRegressor.predict(x_test)
# Evaluate your Model
[67]:
from sklearn.metrics import r2_score

[68]:
print('Training Accuracy : ',r2_score(y_train,y_pred_train))
print()
print('Test Accuracy : ',r2_score(y_test,y_pred_test))
Training Accuracy :  1.0

Test Accuracy :  0.8978427744373091
# Since I am getting 100% in training accuracy so this is an overfitting variance is okay but 100 % I am getting training accuracy - K-Fold

Since I am getting 100% in training accuracy so this is an overfitting variance is okay but 100 % I am getting training accuracy - K-Fold¶

# Using Cross Validation Method - K fold Method
[69]:
from sklearn.model_selection import cross_val_score
Training_Accuracy = cross_val_score(dtRegressor,x_train,y_train,cv=10)
print(Training_Accuracy)

[  0.75859449   0.8686139    0.54063583   0.87707088   0.96925619
   0.26165478   0.92993926   0.27110755 -21.22435468   0.92487939]
[70]:
print(Training_Accuracy.mean())
-1.482260240613908
# linear Regression Model
[75]:

from sklearn.linear_model import LinearRegression
linear = LinearRegression()
linear.fit(x_train,y_train)
[75]:
LinearRegression()
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LinearRegression()
[76]:
#Predict the train and test with Linear model
y_pred_train_lr = linear.predict(x_train)
y_pred_test_lr = linear.predict(x_test)
[77]:
print('Training Accuracy : ',r2_score(y_train,y_pred_train_lr))
print()
print('Test Accuracy : ',r2_score(y_test,y_pred_test_lr))
Training Accuracy :  0.9419507593691141

Test Accuracy :  0.9616053937220067
# Random Forest Regressor Model
[83]:
from sklearn.ensemble import RandomForestRegressor
rf_regressor = RandomForestRegressor(n_estimators=500) #100 decision tree I am building that's why it is called Ensemble
rf_regressor.fit(x_train,y_train)
[83]:
RandomForestRegressor(n_estimators=500)
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RandomForestRegressor(n_estimators=500)
[84]:
#Predict the train and test with Linear model
y_pred_train_rf = rf_regressor.predict(x_train)
y_pred_test_rf = rf_regressor.predict(x_test)
[85]:
print('Training Accuracy : ',r2_score(y_train,y_pred_train_rf))
print()
print('Test Accuracy : ',r2_score(y_test,y_pred_test_rf))
Training Accuracy :  0.9855904843449871

Test Accuracy :  0.9438374607010358
# Classification Problem

Classification Problem¶

[88]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
[89]:
df = pd.read_csv('penguins_lter.csv')
[90]:
df.head()
[90]:
studyName Sample Number Species Region Island Stage Individual ID Clutch Completion Date Egg Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo) Comments
0 PAL0708 1 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N1A1 Yes 11/11/2007 39.1 18.7 181 . MALE NaN NaN Not enough blood for isotopes.
1 PAL0708 2 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N1A2 Yes 11/11/2007 39.5 17.4 186 3800 FEMALE 8.94956 -24.69454 NaN
2 PAL0708 3 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N2A1 Yes 11/16/2007 40.3 18.0 . 3250 FEMALE 8.36821 -25.33302 NaN
3 PAL0708 4 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N2A2 Yes 11/16/2007 NaN NaN NaN NaN NaN NaN NaN Adult not sampled.
4 PAL0708 5 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N3A1 Yes 11/16/2007 36.7 19.3 193 3450 FEMALE 8.76651 -25.32426 NaN
[91]:
df.shape
[91]:
(344, 17)
[92]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 17 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   studyName            344 non-null    object 
 1   Sample Number        344 non-null    int64  
 2   Species              344 non-null    object 
 3   Region               344 non-null    object 
 4   Island               344 non-null    object 
 5   Stage                344 non-null    object 
 6   Individual ID        344 non-null    object 
 7   Clutch Completion    344 non-null    object 
 8   Date Egg             344 non-null    object 
 9   Culmen Length (mm)   342 non-null    float64
 10  Culmen Depth (mm)    342 non-null    float64
 11  Flipper Length (mm)  342 non-null    object 
 12  Body Mass (g)        342 non-null    object 
 13  Sex                  334 non-null    object 
 14  Delta 15 N (o/oo)    330 non-null    float64
 15  Delta 13 C (o/oo)    331 non-null    float64
 16  Comments             26 non-null     object 
dtypes: float64(4), int64(1), object(12)
memory usage: 45.8+ KB
[93]:
df.columns
[93]:
Index(['studyName', 'Sample Number', 'Species', 'Region', 'Island', 'Stage',
       'Individual ID', 'Clutch Completion', 'Date Egg', 'Culmen Length (mm)',
       'Culmen Depth (mm)', 'Flipper Length (mm)', 'Body Mass (g)', 'Sex',
       'Delta 15 N (o/oo)', 'Delta 13 C (o/oo)', 'Comments'],
      dtype='object')
[94]:
df.head(2)
[94]:
studyName Sample Number Species Region Island Stage Individual ID Clutch Completion Date Egg Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo) Comments
0 PAL0708 1 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N1A1 Yes 11/11/2007 39.1 18.7 181 . MALE NaN NaN Not enough blood for isotopes.
1 PAL0708 2 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N1A2 Yes 11/11/2007 39.5 17.4 186 3800 FEMALE 8.94956 -24.69454 NaN
[95]:
#Pre Pruning Approach
df.drop(['studyName','Sample Number','Individual ID','Date Egg','Comments'],axis=1,inplace = True)
[96]:
df.head(2)
[96]:
Species Region Island Stage Clutch Completion Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo)
0 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage Yes 39.1 18.7 181 . MALE NaN NaN
1 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage Yes 39.5 17.4 186 3800 FEMALE 8.94956 -24.69454
[97]:
df.shape
[97]:
(344, 12)
# Data Preprocessing

Data Preprocessing¶

[98]:
df.isnull().sum()
[98]:
Species                 0
Region                  0
Island                  0
Stage                   0
Clutch Completion       0
Culmen Length (mm)      2
Culmen Depth (mm)       2
Flipper Length (mm)     2
Body Mass (g)           2
Sex                    10
Delta 15 N (o/oo)      14
Delta 13 C (o/oo)      13
dtype: int64
[99]:
df.isnull().sum()/len(df)*100
[99]:
Species                0.000000
Region                 0.000000
Island                 0.000000
Stage                  0.000000
Clutch Completion      0.000000
Culmen Length (mm)     0.581395
Culmen Depth (mm)      0.581395
Flipper Length (mm)    0.581395
Body Mass (g)          0.581395
Sex                    2.906977
Delta 15 N (o/oo)      4.069767
Delta 13 C (o/oo)      3.779070
dtype: float64
[100]:
#all the observation is less than 5% ,in this case we are not supposed to remove the variable,we have to do imputation method
#20% missing data or 25% missing data before dropping the variable first take the confirmation from the business stakeholder
#take their concurrence ,ask them if the data is available otherwise we have to drop that variable,otherwise it will create bias

[101]:
# Finding the unique values in given dataset
for i in df.columns:
print("*******************************"
,i,"*******************************")
print()
print(set(df[i].to_list()))
******************************* Species *******************************

{'Gentoo penguin (Pygoscelis papua)', 'Chinstrap penguin (Pygoscelis antarctica)', 'Adelie Penguin (Pygoscelis adeliae)'}
******************************* Region *******************************

{'Anvers'}
******************************* Island *******************************

{'Torgersen', 'Biscoe', 'Dream'}
******************************* Stage *******************************

{'Adult, 1 Egg Stage'}
******************************* Clutch Completion *******************************

{'No', 'Yes'}
******************************* Culmen Length (mm) *******************************

{49.4, 49.9, 37.3, 45.7, 50.4, 32.1, 33.1, 33.5, 35.6, 36.2, 35.7, 38.1, 35.2, 40.2, 41.5, 34.1, 34.6, 36.7, 37.8, 38.9, 39.5, 40.3, 42.0, 41.1, 42.5, 46.0, 44.1, 45.8, 46.5, 50.0, 51.3, 52.7, 51.7, 52.0, 58.0, 54.2, 55.8, 59.6, 47.7, 47.2, 48.2, 45.0, 50.2, nan, 43.8, 34.4, 34.5, 34.0, 35.9, 35.3, 35.0, 35.1, 35.5, 36.6, 36.4, 36.5, 36.0, 36.9, 37.7, 37.9, 37.2, 37.6, 37.0, 38.6, 38.7, 38.2, 38.8, 37.5, 39.1, 39.3, 39.2, 39.8, 39.6, 39.0, 39.7, 40.6, 40.5, 40.9, 40.8, 40.1, 41.4, 41.3, 41.6, 41.8, 41.0, 42.2, 42.3, 42.8, 42.1, 42.9, 43.2, 42.7, 43.1, 43.5, 43.3, 44.5, 44.4, 44.0, 45.6, 45.4, 45.2, 45.9, 45.5, 46.1, 46.6, 46.4, 46.7, 46.9, 47.0, 47.5, 47.6, 46.2, 46.8, 48.5, 48.1, 48.7, 48.4, 47.8, 49.2, 49.5, 49.7, 49.0, 49.8, 50.5, 50.3, 50.6, 50.9, 50.8, 51.0, 50.1, 51.5, 51.4, 50.7, 52.8, 52.2, 51.9, 47.3, 51.1, 53.5, 52.5, 52.1, 43.6, 53.4, 54.3, 55.9, 55.1, 48.8, 42.6, 49.3, 45.1, 45.3, 42.4, 43.4, 44.9, 48.6, 40.7, 49.6, 49.1, 41.7, 38.5, 46.3, 47.4, 38.3, 36.3, 36.8, nan}
******************************* Culmen Depth (mm) *******************************

{13.2, 14.1, 15.9, 16.7, 17.4, 18.4, 19.0, 20.7, 21.5, 18.7, 19.3, 18.0, 20.6, 17.8, 19.6, 20.2, 21.2, 21.1, 13.5, 14.5, 14.0, 15.5, 15.0, 16.0, 16.4, 16.9, 16.6, 16.1, 17.5, 17.1, 17.6, 17.9, 17.0, 18.1, 18.9, 18.6, 18.5, 19.1, 19.5, 19.4, 19.9, 20.0, 20.5, 20.1, 13.4, 13.9, 14.4, 14.9, 15.4, nan, 13.3, 13.8, 13.7, 14.3, 14.8, 14.2, 14.7, 15.2, 15.3, 15.7, 15.8, 16.2, 16.8, 16.3, 17.7, 17.3, 17.2, 18.3, 18.8, 18.2, 19.8, 19.2, 19.7, 20.3, 20.8, nan, 16.5, 13.6, 13.1, 14.6, 15.1, 15.6}
******************************* Flipper Length (mm) *******************************

{'172', '219', '.', '229', '181', '180', '205', '202', '228', '193', '231', '179', '174', '176', '220', '215', '184', '208', '191', '211', '201', '187', '206', '221', '225', '223', '209', '230', '194', '178', '200', '198', '189', '185', '213', '224', '182', '210', '222', '190', '226', '186', '197', '207', nan, '183', '188', '192', '199', '212', '218', '203', '195', '214', '217', '196', '216'}
******************************* Body Mass (g) *******************************

{'5000', '.', '3500', '3100', '3675', '5750', '4575', '4150', '5850', '4625', '3350', '4275', '2975', '3625', '2925', '3300', '5650', '4650', '3050', '3600', '4050', '5050', '3775', nan, '5800', '5300', '6000', '4850', '4775', '3850', '2900', '5550', '3325', '4600', '3700', '4000', '3450', '3875', '5450', '5150', '4900', '5500', '4975', '4500', '4375', '3275', '3200', '4100', '4400', '4750', '6300', '3150', '5700', '2850', '4725', '5600', '3075', '3000', '5200', '3175', '3475', '4075', '5950', '3725', '3975', '5250', '4450', '6050', '3825', '3800', '3900', '4950', '5400', '3400', '3650', '3750', '4700', '4675', '3550', '4300', '5350', '3525', '4875', '3425', '4250', '2700', '4925', '4475', '4350', '5100', '4200', '3250', '3575', '4550', '3950', '4800'}
******************************* Sex *******************************

{'.', 'FEMALE', 'MALE', nan}
******************************* Delta 15 N (o/oo) *******************************

{7.88863, 8.93465, 9.98044, 8.46894, 8.90027, 8.47781, 8.19539, 8.94956, 8.36821, 8.76651, 8.66496, 9.18718, 10.02019, 7.88494, 8.94332, 8.8964, 8.63243, 8.35396, 8.01979, 9.4606, 9.27158, 9.07826, 8.79665, 8.9846, 9.46929, 9.62357, 9.65061, 8.70642, 8.2066, 8.15426, 8.29671, 8.30515, 8.14756, 8.38404, 8.33524, 8.32718, 9.42666, 9.49283, 9.45827, 9.29808, 9.22537, 8.39299, 7.90436, 8.65914, 8.30231, 7.99184, 8.75984, 8.65015, 8.51951, 8.08138, 8.74802, 9.25769, 9.22286, 9.21292, 8.87988, 9.14863, 10.02372, 9.11066, 9.07825, 8.63551, nan, 8.27141, nan, 8.96472, 8.96436, 8.24651, 8.65803, 8.63259, nan, 8.58063, 8.27376, nan, 8.01485, 8.2345, 8.23468, 8.86495, 8.83352, 8.27102, 7.76843, 7.69778, 7.993, 8.24694, 8.24515, 8.88098, 8.64701, 8.58487, 8.14567, 8.9399, 9.07878, 8.37615, 8.27595, 8.29226, 8.12311, 8.41017, 8.74647, 8.93997, nan, 7.8962, 8.97025, 8.62623, 8.86853, 8.73762, 8.18658, 9.25177, 9.46819, 8.40327, 8.60447, 9.02657, 9.04296, 9.415, 8.45738, 7.79958, 8.24691, 8.59147, 8.30166, 7.8208, 7.96935, 8.81668, 8.68747, 8.47257, 8.49854, 8.47938, 9.56534, 9.00642, 9.32277, 9.59462, 9.05674, 9.36799, 9.06829, nan, 8.04111, 7.90971, 7.96621, 7.89744, 8.48367, 8.03659, 8.5964, 8.04787, 8.79787, 8.79581, 9.22033, 8.67538, 9.15308, 9.05736, 9.43684, 9.32169, 8.36701, 8.62264, 8.25818, 8.36936, 8.85664, 8.41151, 7.63884, 8.13643, 8.19579, 8.24253, 8.19749, 8.52566, 8.47829, 9.43146, 9.08458, 9.36493, 9.39305, 9.43782, 9.18021, 8.58319, 8.3639, 8.85699, 8.39459, 8.94365, 7.92358, 7.97408, 8.56192, 8.77322, 8.08354, nan, 7.9953, 7.84057, 8.55583, 8.43423, 8.57087, 8.97533, 8.83502, 9.41131, 9.01079, 9.23196, 9.32105, 8.1631, 9.2881, 9.59245, 9.26715, 9.70465, 8.15566, 8.16582, 8.20106, nan, 8.33825, nan, 8.48204, 8.717, 8.90002, 8.43951, 8.47067, 9.93727, 9.68933, 9.51929, 9.29078, 9.03935, 9.61734, 8.4207, nan, 8.12691, nan, 8.24246, 7.77672, 7.6322, 7.6887, 8.90723, 8.98705, 8.35802, 8.41837, 8.80967, 8.10417, 9.19031, 9.37369, 9.49645, 9.63954, 9.69756, 9.4618, 9.74144, 8.19101, 8.95063, 8.39867, 8.56708, 8.57199, 8.26548, 7.83733, 7.63452, 7.82381, 7.8881, 8.30817, 8.2554, 8.6287, 8.68744, 8.20042, 8.55868, 9.53262, 9.6895, 9.46985, 8.63604, 8.47827, 9.72764, 9.66523, 8.84451, 9.8059, 9.77528, 8.48095, 8.3118, 8.71078, 8.10231, 8.46531, nan, 8.77018, 8.32359, 8.14705, 8.47173, 8.65466, 8.92069, 8.45167, 9.13362, 9.35416, 9.74492, 9.11616, 9.18985, 9.51784, 9.36392, 9.2151, 9.75486, 8.2993, 8.51362, 7.68528, nan, nan, 8.27428, 8.61651, 8.14776, 8.80186, 8.28601, 9.18528, 9.10702, 9.88809, 9.80589, 9.50772, 9.49106, 9.31735, 9.0233, 9.35138, 8.64931, 8.78557, 8.46616, 8.21634, 8.35078, 8.10277, 7.96491, 8.3554, 8.63701, 8.13746, 8.22673, 8.38289, 9.11006, 9.28153, 9.79532, 9.12277, 8.66271, 9.17847, 9.108, 10.02544, 9.63074, 9.14382, 8.91434, 8.60092, 8.03624, 8.72037, 8.50153, 8.88942, 8.53018, 8.11238, 9.37608, 9.34089, 9.30722, 8.07137, 8.95998, 9.36668, 9.2372, 9.04218, 9.02642, 8.63488, 8.49662, 8.38324, 9.23408, 8.48789, 8.85562, 8.49915, 8.56674}
******************************* Delta 13 C (o/oo) *******************************

{-26.06594, -25.03469, -26.33867, -26.18466, -26.18444, -26.20372, -26.79093, -26.57585, -26.18599, -26.2253, -25.95399, -25.62618, -25.89677, -25.8306, -25.33302, -24.65859, -25.69199, -24.45721, -25.39587, -26.83006, -24.80526, -26.06943, -26.75621, -24.69638, -24.43062, -25.38157, -26.57563, -25.32829, -25.27385, -25.54456, -26.7489, -26.05621, -25.48025, -25.79203, -26.08165, -25.77951, -26.65931, -26.07021, -26.06967, -26.22664, nan, nan, -26.08547, -26.68867, -25.22664, -26.86127, -26.20538, -26.03679, -26.78733, -26.3433, -25.29805, nan, -26.01363, -26.32909, -26.59467, -26.65359, -26.22227, -25.23319, -25.5139, -26.79846, -25.73722, -26.7699, -24.61867, -24.805, -24.29229, -24.60882, -24.36088, -24.59467, -24.31198, -24.40753, -24.62717, -24.70615, nan, -26.11657, -25.85203, -26.41218, -25.77264, -25.95541, -26.23886, -25.80208, nan, nan, -25.88547, -26.05756, nan, -26.35425, -26.15531, -26.11244, nan, -26.76821, -26.03495, -26.06209, -26.63085, -26.38085, -25.81513, -25.3933, -25.4276, -25.81012, -25.49448, -24.90816, -24.52896, -25.19837, -24.52698, -25.11223, -24.59996, -24.6844, -24.55644, nan, -24.45195, -26.21019, nan, -26.84374, -26.13971, -26.27853, -26.70968, -25.32176, -26.42406, -26.57941, -25.54976, -25.6821, -24.4228, -26.44787, -25.14591, -26.1165, -26.38396, -25.97696, -26.19444, -25.52473, -26.84506, -25.01185, -26.01549, -26.84272, -26.5929, -26.84415, -26.37809, -26.30037, -25.21799, -25.57956, -26.30019, -25.37899, -25.37746, -24.26375, -24.59066, -24.6879, -24.34684, -24.25255, -24.89958, -26.24369, -25.06691, -26.63405, -26.13832, -25.0602, -25.46172, -25.39369, -26.09635, -25.36288, -24.53494, -26.2766, -25.79529, -26.17213, -26.60436, -24.66867, -24.48153, -25.89741, -24.78984, -26.06203, -25.89834, -24.10255, -26.18161, -26.22848, -26.09989, -26.61601, -26.55602, -26.46254, -26.0245, -25.22588, -26.79053, -25.50811, -26.55351, -26.23027, -24.69454, -24.7557, -24.49433, -24.41562, -25.04169, -24.48403, -24.64162, -24.54903, -26.60023, -26.18763, -25.40075, nan, -26.07081, nan, -26.39677, -26.69166, -25.23453, -25.46782, -25.46327, -25.42621, -25.07683, -26.89644, nan, -26.11046, -25.11609, -26.15775, -26.12417, -26.11199, -26.86352, -26.67799, -25.39181, -25.45171, -26.61414, -25.83352, -25.38017, -24.57994, -24.56481, -24.73735, -25.50562, -25.42826, -24.66259, -26.51382, -24.23592, -23.78767, -24.84059, -23.90309, -25.49523, -25.86482, -26.24837, -25.48383, -25.76147, -26.02002, -25.88156, -26.86485, -25.46569, -26.04726, -26.72751, -25.21315, -26.28055, -25.13993, -25.03474, -25.29856, -25.23061, -26.60484, -26.21569, -26.32601, -26.66958, -26.54718, -25.79189, -25.4168, -26.45978, -25.0102, -25.18543, -24.404, -25.01745, -25.61039, -25.00169, -24.64335, -24.97134, -24.47142, -24.66188, -24.31912, -24.86594, -26.07821, -26.01152, -25.53768, -25.69195, -25.09383, -26.16524, -26.3146, -26.40943, -26.84166, -26.74809, -26.61075, -26.44815, -25.12255, -26.38092, -25.96013, -26.9547, -26.27573, -24.77227, -26.42018, -25.09368, -26.07188, -25.52627, -26.04117, -26.21651, -26.74249, -25.19017, -26.69543, -26.50086, -26.11969, -25.32426, -25.69327, -26.38986, -25.0639, -26.78958, -26.68311, -26.7765, -26.1396, -26.09294, -25.1455, -25.60826, -25.57647, -25.88798, -25.79549, -24.7294, -24.35575, -24.65786, -24.38933, -25.03492, -24.30229, -24.17282, -24.68741, -24.36202, -24.3613, -26.15003, -26.49288, -26.61788, -26.71199, -26.79358, -24.16566, -24.59897, -26.23613, -25.98843, -24.38751, -26.12989, -27.01854, -26.5387, -26.36678, -24.54704, -24.59513, -26.72791, -26.42563, -25.70711, -26.03442, -25.3947, -26.8154, -24.45189, -24.90024, -23.89017, -26.48973, -26.36863, -26.70783}
[102]:
df['Flipper Length (mm)'].mode()
[102]:
0    190
Name: Flipper Length (mm), dtype: object
[103]:
df['Flipper Length (mm)'] = np.where(df['Flipper Length (mm)']=='.',
'190',
df['Flipper Length (mm)'])
[104]:
df['Body Mass (g)'].mode()
[104]:
0    3800
Name: Body Mass (g), dtype: object
[105]:
df['Body Mass (g)'] = np.where(df['Body Mass (g)']=='.',
'3800',
df['Body Mass (g)'])
[106]:
df['Sex'].mode()
[106]:
0    MALE
Name: Sex, dtype: object
[107]:
df['Sex'] = np.where(df['Sex']=='.',
'MALE',
df['Sex'])
[108]:
for i in df.columns:
print("*******************************"
,i,"*******************************")
print()
print(set(df[i].to_list()))
******************************* Species *******************************

{'Gentoo penguin (Pygoscelis papua)', 'Chinstrap penguin (Pygoscelis antarctica)', 'Adelie Penguin (Pygoscelis adeliae)'}
******************************* Region *******************************

{'Anvers'}
******************************* Island *******************************

{'Torgersen', 'Biscoe', 'Dream'}
******************************* Stage *******************************

{'Adult, 1 Egg Stage'}
******************************* Clutch Completion *******************************

{'No', 'Yes'}
******************************* Culmen Length (mm) *******************************

{49.4, 49.9, 37.3, 45.7, 50.4, 32.1, 33.1, 33.5, 35.6, 36.2, 35.7, 38.1, 35.2, 40.2, 41.5, 34.1, 34.6, 36.7, 37.8, 38.9, 39.5, 40.3, 42.0, 41.1, 42.5, 46.0, 44.1, 45.8, nan, 50.0, 51.3, 52.7, 51.7, 52.0, 58.0, 54.2, 55.8, 59.6, 47.7, 47.2, 48.2, nan, 45.0, 50.2, 43.8, 34.4, 34.5, 34.0, 35.9, 35.3, 35.1, 35.0, 35.5, 36.6, 36.4, 36.5, 36.0, 36.9, 37.7, 37.9, 37.2, 37.6, 37.0, 38.6, 38.7, 38.2, 38.8, 37.5, 39.1, 39.3, 39.2, 39.8, 39.6, 39.0, 39.7, 40.6, 40.5, 40.9, 40.8, 40.1, 41.4, 41.3, 41.6, 41.8, 41.0, 42.2, 42.3, 42.8, 42.1, 42.9, 43.2, 42.7, 43.1, 43.5, 43.3, 44.5, 44.4, 44.0, 45.6, 45.4, 45.2, 45.9, 45.5, 46.5, 46.1, 46.6, 46.4, 46.7, 47.0, 47.5, 47.6, 46.9, 46.2, 48.5, 48.1, 48.7, 48.4, 47.8, 49.2, 49.5, 49.7, 49.0, 49.8, 50.5, 50.3, 50.6, 50.9, 50.8, 51.0, 50.1, 51.5, 51.4, 50.7, 52.8, 52.2, 51.9, 47.3, 51.1, 53.5, 52.5, 52.1, 43.6, 53.4, 54.3, 55.9, 55.1, 48.8, 42.6, 49.3, 45.1, 45.3, 42.4, 43.4, 44.9, 48.6, 40.7, 49.6, 49.1, 41.7, 38.5, 46.8, 46.3, 47.4, 38.3, 36.3, 36.8}
******************************* Culmen Depth (mm) *******************************

{13.2, 14.1, 15.9, 16.7, 17.4, 18.4, 19.0, 20.7, 21.1, 21.5, 18.7, 19.3, 18.0, 20.6, 19.6, 20.2, 21.2, 13.5, 14.5, 14.0, 15.5, 15.0, 16.0, 16.5, 16.4, 16.9, 16.6, 17.0, 17.5, 16.1, 17.1, 17.6, 18.1, 18.9, 18.6, 17.9, 18.5, 19.1, 19.5, 19.4, 19.9, 20.0, 20.5, 20.1, nan, 13.4, 13.9, 14.4, 14.9, 15.4, nan, 13.3, 13.8, 13.7, 14.3, 14.8, 14.2, 14.7, 15.2, 15.3, 15.7, 15.8, 16.2, 16.8, 16.3, 17.7, 17.8, 17.3, 17.2, 18.3, 18.8, 18.2, 19.8, 19.2, 19.7, 20.3, 20.8, 13.6, 13.1, 14.6, 15.1, 15.6}
******************************* Flipper Length (mm) *******************************

{'172', '219', '229', '181', '180', '205', '202', '228', '193', '231', '179', '174', '176', '220', '215', '184', '208', '191', '211', '201', '187', '206', '221', '225', '223', '209', '230', '194', '178', '200', '198', '189', '185', '213', '224', '182', '210', '222', '190', '226', '186', '197', '207', nan, '183', '188', '192', '199', '212', '218', '203', '195', '214', '217', '196', '216'}
******************************* Body Mass (g) *******************************

{'5000', '3500', '3100', '3675', '5750', '4575', '4150', '5850', '4625', '3350', '4275', '2975', '3625', '2925', '3300', '5650', '4650', '3050', '3600', '4050', '5050', '3775', nan, '5800', '5300', '6000', '4850', '4775', '3850', '2900', '5550', '3325', '4600', '3700', '4000', '3450', '3875', '5450', '5150', '4900', '5500', '4975', '4500', '4375', '3275', '3200', '4100', '4400', '4750', '6300', '3150', '5700', '2850', '4725', '5600', '3075', '3000', '5200', '3175', '3475', '4075', '5950', '3725', '3975', '5250', '4450', '6050', '3825', '3800', '3900', '4950', '5400', '3400', '3650', '3750', '4700', '4675', '3550', '4300', '5350', '3525', '4875', '3425', '4250', '2700', '4925', '4475', '4350', '5100', '4200', '3250', '3575', '4550', '3950', '4800'}
******************************* Sex *******************************

{'FEMALE', 'MALE', nan}
******************************* Delta 15 N (o/oo) *******************************

{7.88863, 8.93465, 9.98044, 8.46894, 8.90027, 8.47781, 8.19539, 8.67538, 8.94956, 8.36821, 8.76651, 9.18718, 10.02019, 7.88494, 8.94332, 8.8964, 8.63243, 8.35396, 8.01979, 9.4606, 9.27158, 9.07826, 8.79665, 8.9846, 9.46929, 9.62357, 9.65061, 8.70642, 8.2066, 8.15426, 8.29671, 8.30515, 8.14756, 8.38404, 8.33524, 8.32718, 9.42666, 9.22537, 9.45827, 9.29808, 8.39299, 9.49283, 7.90436, 8.65914, 8.30231, 7.99184, 8.75984, 8.65015, 8.51951, 8.08138, 8.74802, 9.14863, 9.25769, 9.22286, 9.21292, 8.87988, 10.02372, 9.11066, nan, 8.63551, 9.07825, 8.27141, 8.96472, 8.96436, 8.24651, 8.65803, 8.63259, 8.58063, nan, 8.27376, nan, 8.01485, 8.2345, 8.23468, 8.86495, 8.83352, 8.27102, 7.76843, 7.69778, 7.993, 8.24694, 8.24515, 8.88098, 8.64701, 8.58487, 8.14567, 8.9399, 9.07878, 8.37615, 8.27595, 8.29226, 8.12311, 8.41017, nan, 8.74647, 8.93997, nan, nan, 7.8962, 8.97025, 8.62623, 8.86853, 8.73762, 8.18658, 9.25177, 9.46819, 8.40327, 8.60447, 9.02657, 9.04296, 9.415, 8.45738, nan, 7.79958, 8.24691, 8.59147, 8.30166, nan, 7.8208, 7.96935, 8.81668, 8.68747, 8.47257, 8.49854, 8.47938, 9.56534, 9.00642, 9.32277, 9.59462, 9.05674, 9.36799, 9.06829, 8.04111, 7.90971, 7.96621, 7.89744, 8.03659, 8.5964, 8.79787, 8.79581, 8.04787, 9.22033, 9.15308, 9.05736, 9.43684, 8.48367, 9.32169, 8.36701, 8.62264, 8.25818, 8.45167, 8.36936, 8.85664, 8.41151, 7.63884, 8.13643, 8.19579, 8.24253, 8.19749, 8.52566, 8.47829, 9.43146, 9.08458, 9.36493, 9.39305, 9.43782, 9.18021, 8.58319, 8.3639, 8.85699, 8.39459, 8.94365, 7.92358, 7.97408, 8.56192, 8.77322, 8.08354, 7.9953, 7.84057, 8.55583, 8.43423, 8.57087, 8.97533, 8.83502, 9.41131, 9.01079, 9.23196, 9.32105, 8.1631, 9.2881, 9.59245, 9.26715, 9.70465, 8.15566, 8.16582, 8.20106, 8.33825, 8.48204, 8.717, 8.90002, 8.43951, 8.47067, 9.93727, 9.68933, 9.51929, 9.29078, 9.03935, 9.61734, 8.4207, 8.12691, nan, 8.24246, 7.77672, 7.6322, 7.6887, 8.90723, 8.98705, 8.35802, 8.41837, 8.80967, 8.10417, 9.19031, 9.37369, 9.49645, 9.63954, 9.69756, 9.4618, 9.74144, 8.19101, 8.95063, nan, 8.39867, 8.56708, 8.57199, 8.26548, 7.83733, 7.63452, 7.82381, 7.8881, 8.30817, 8.2554, 8.6287, 8.68744, 8.20042, 8.55868, 9.53262, 9.6895, 9.46985, 8.63604, 8.47827, 9.72764, 9.66523, 8.84451, 9.8059, 9.77528, 8.48095, 8.3118, 8.71078, 8.10231, 8.46531, 8.77018, nan, nan, 8.32359, 8.66496, 8.14705, 8.47173, 8.65466, 8.92069, 9.13362, 9.35416, 9.74492, 9.18985, 9.51784, 9.11616, 9.36392, 9.2151, 9.75486, 8.2993, 8.51362, nan, 7.68528, nan, 8.27428, 8.61651, 8.14776, 8.80186, 8.28601, 9.10702, 9.88809, 9.80589, 9.18528, 9.50772, 9.49106, 9.31735, 9.0233, 9.35138, 8.64931, 8.78557, 8.46616, 8.21634, 8.35078, 8.10277, 7.96491, 8.3554, 8.63701, 8.13746, 8.22673, 8.38289, 9.11006, 9.28153, 9.79532, 9.12277, 8.66271, 9.17847, 9.108, 10.02544, 9.63074, 9.14382, 8.91434, 8.60092, 8.03624, 8.72037, 8.50153, 8.88942, 8.53018, 8.11238, 9.37608, 9.34089, 9.30722, 8.07137, 8.95998, 9.36668, 9.2372, 9.04218, 9.02642, 8.63488, 8.49662, 8.38324, 9.23408, 8.48789, 8.85562, 8.49915, 8.56674}
******************************* Delta 13 C (o/oo) *******************************

{-26.06594, -25.03469, -26.33867, -26.18466, -26.18444, -26.20372, -26.79093, -26.57585, -26.2253, -26.39677, -25.95399, -25.62618, -25.89677, -25.8306, -25.33302, -24.65859, -25.69199, -24.45721, -25.39587, -26.83006, -24.80526, -26.06943, -26.75621, -24.69638, -24.43062, -25.38157, -26.57563, -25.32829, -25.27385, -25.54456, -26.7489, -26.05621, -25.48025, -25.79203, -26.08165, nan, -25.77951, -26.65931, -26.07021, -26.06967, -26.22664, -26.08547, -26.68867, -25.22664, -26.86127, nan, -26.20538, -26.03679, -26.78733, -26.3433, -25.29805, -26.01363, -26.32909, -26.59467, -26.65359, -26.22227, -25.5139, -25.09368, -26.79846, -25.73722, -26.7699, -24.61867, -24.805, -24.29229, -24.60882, -24.36088, -24.59467, -24.31198, -24.40753, -24.62717, -24.70615, nan, -26.11657, -25.85203, -26.41218, -25.77264, nan, -25.95541, nan, -26.23886, -25.80208, -26.18599, nan, -25.88547, -26.05756, -26.35425, -26.15531, -26.11244, nan, -26.76821, -26.03495, -26.06209, -26.63085, -26.38085, -25.81513, -25.3933, -25.4276, -25.81012, -25.49448, -24.90816, -24.52896, -25.19837, -25.11223, -24.52698, nan, -24.59996, -24.6844, -24.55644, -24.45195, -26.21019, nan, -26.84374, -26.13971, -26.27853, -26.70968, -25.32176, -26.42406, -26.57941, -25.54976, -25.6821, -24.4228, -26.44787, -25.14591, -26.1165, -26.38396, -25.97696, -26.19444, -25.52473, -26.84506, -25.01185, -26.01549, -26.84272, -26.5929, -26.84415, -26.37809, -26.30037, -25.21799, -25.57956, -26.30019, -25.37899, -25.37746, -24.26375, -24.59066, -24.6879, -24.34684, -24.25255, -24.89958, -26.24369, -25.06691, -26.63405, -26.13832, -25.0602, -25.46172, -25.39369, -26.09635, -25.36288, -24.53494, -26.2766, -25.79529, -26.17213, -26.60436, -24.66867, -24.48153, -25.89741, -26.06203, -25.89834, -24.10255, -26.18161, -26.22848, -26.09989, -26.61601, -26.55602, -26.46254, -26.0245, -25.22588, -26.79053, -25.50811, -26.55351, -26.23027, -24.69454, -24.7557, -24.49433, -24.41562, -25.04169, -24.48403, -24.64162, -24.54903, -26.60023, -26.18763, -25.40075, -26.07081, -26.69166, -25.23453, -25.46782, -25.46327, nan, -25.42621, nan, -25.07683, -26.89644, -26.11046, -25.11609, -26.15775, -26.12417, -26.11199, -26.86352, -26.67799, -25.39181, -25.45171, -26.61414, -25.83352, -25.38017, -24.57994, -24.56481, -24.73735, -25.50562, -25.42826, -24.66259, -26.51382, -24.23592, -23.78767, -24.84059, -23.90309, -25.49523, -25.86482, -26.24837, -25.48383, -25.76147, -26.02002, -25.88156, -26.86485, -25.46569, -26.04726, -26.72751, -25.21315, -26.28055, -25.13993, -25.0102, -25.03474, -25.29856, -25.23061, -26.60484, -26.21569, -26.32601, -26.66958, -26.54718, -25.79189, -25.4168, -26.45978, -25.18543, -25.01745, -24.404, -25.61039, -25.00169, -24.64335, -24.97134, -24.47142, -24.66188, -24.31912, -24.86594, -24.78984, -26.07821, -26.01152, -25.53768, -25.69195, -25.09383, -26.16524, -26.3146, -26.40943, nan, -26.84166, -26.74809, -25.23319, -26.61075, -26.44815, -25.12255, -26.38092, -25.96013, -26.9547, -26.27573, -24.77227, -26.42018, -26.07188, -25.52627, -26.04117, -26.21651, -26.74249, -25.19017, -26.69543, nan, -26.50086, -26.11969, -25.32426, -25.69327, -26.38986, -25.0639, -26.78958, -26.68311, -26.7765, -26.1396, -26.09294, -25.1455, -25.60826, -25.57647, -25.88798, -25.79549, -24.7294, -24.35575, -24.65786, -24.38933, -25.03492, -24.30229, -24.17282, -24.68741, -24.36202, -24.3613, -26.15003, -26.49288, -26.61788, -26.71199, -26.79358, -24.16566, -24.59897, -26.23613, -25.98843, -24.38751, -26.12989, -27.01854, -26.5387, -26.36678, -24.54704, -24.59513, -26.72791, -26.42563, -25.70711, -26.03442, -25.3947, -26.8154, -24.45189, -24.90024, -23.89017, -26.48973, -26.36863, -26.70783}
[109]:
df['Flipper Length (mm)'] = pd.to_numeric(df['Flipper Length (mm)'])
[110]:
df['Body Mass (g)'] = pd.to_numeric(df['Body Mass (g)'])
[111]:
for i in df.columns:
print("*******************************"
,i,"*******************************")
print()
print(set(df[i].to_list()))
******************************* Species *******************************

{'Gentoo penguin (Pygoscelis papua)', 'Chinstrap penguin (Pygoscelis antarctica)', 'Adelie Penguin (Pygoscelis adeliae)'}
******************************* Region *******************************

{'Anvers'}
******************************* Island *******************************

{'Torgersen', 'Biscoe', 'Dream'}
******************************* Stage *******************************

{'Adult, 1 Egg Stage'}
******************************* Clutch Completion *******************************

{'No', 'Yes'}
******************************* Culmen Length (mm) *******************************

{49.4, 49.9, nan, 45.7, 50.4, 32.1, 33.1, 33.5, 35.6, 36.2, 35.7, 38.1, 35.2, 40.2, 41.5, 34.1, 34.6, 36.7, 37.8, 38.7, 38.9, 39.5, 40.3, 42.0, 42.5, 46.0, 44.1, 45.8, 46.5, 50.0, 51.3, 52.7, 51.7, 52.0, 58.0, 54.2, 55.8, 59.6, 47.7, 47.2, 44.0, 48.2, 45.0, nan, 50.2, 43.8, 34.4, 34.5, 34.0, 35.9, 35.3, 35.0, 35.1, 35.5, 36.6, 36.4, 36.5, 36.0, 36.9, 37.7, 37.9, 37.2, 37.6, 37.0, 38.6, 38.2, 38.8, 37.5, 37.3, 39.1, 39.3, 39.2, 39.8, 39.6, 39.0, 39.7, 40.6, 40.5, 40.9, 41.1, 40.8, 40.1, 41.4, 41.3, 41.6, 41.8, 41.0, 42.2, 42.3, 42.8, 42.1, 42.9, 43.2, 42.7, 43.1, 43.5, 43.3, 44.5, 44.4, 45.6, 45.4, 45.2, 45.9, 45.5, 46.1, 46.6, 46.4, 46.7, 46.9, 47.0, 47.5, 47.6, 46.2, 46.8, 48.5, 48.1, 48.7, 48.4, 47.8, 49.2, 49.5, 49.7, 49.0, 49.8, 50.5, 50.3, 50.6, 50.9, 50.8, 51.0, 50.1, 51.5, 51.4, 50.7, 52.8, 52.2, 51.9, 47.3, 51.1, 53.5, 52.5, 52.1, 43.6, 53.4, 54.3, 55.9, 55.1, 48.8, 42.6, 49.3, 45.1, 45.3, 42.4, 43.4, 44.9, 48.6, 40.7, 49.6, 49.1, 41.7, 38.5, 46.3, 47.4, 38.3, 36.3, 36.8}
******************************* Culmen Depth (mm) *******************************

{13.2, 14.1, 15.9, 16.7, 17.4, 18.4, 19.0, 20.7, 21.5, 18.7, 19.3, 18.0, 20.6, 17.8, 19.6, 20.2, 21.2, 21.1, 13.5, 14.5, 14.0, 15.5, 15.0, 16.0, 16.4, 16.9, 16.6, 16.1, 17.5, 17.1, 17.6, 17.9, 17.0, 18.1, 18.9, 18.6, 18.5, 19.1, 19.5, 19.4, 19.9, 20.0, 20.5, 20.1, nan, 13.4, 13.9, 14.4, 14.9, 15.4, 13.3, 13.8, 13.7, 14.3, 14.8, 14.2, 14.7, 15.2, 15.3, 15.7, 15.8, 16.2, 16.8, 16.3, 17.7, 17.3, 17.2, 18.8, 18.3, 18.2, 19.8, 19.2, 19.7, 20.3, 20.8, 16.5, 13.6, 13.1, 14.6, 15.1, 15.6, nan}
******************************* Flipper Length (mm) *******************************

{172.0, 174.0, 176.0, 178.0, 179.0, 180.0, 181.0, 182.0, 183.0, 184.0, 185.0, 186.0, 187.0, 188.0, 189.0, 190.0, 191.0, 192.0, 193.0, 194.0, 195.0, 196.0, 197.0, 198.0, 199.0, 200.0, nan, 202.0, 203.0, 201.0, 205.0, 206.0, 207.0, 208.0, 209.0, 210.0, 211.0, 212.0, 213.0, 214.0, 215.0, 216.0, 217.0, 218.0, 219.0, 220.0, 221.0, 222.0, 223.0, 224.0, 225.0, 226.0, 228.0, 229.0, 230.0, 231.0, nan}
******************************* Body Mass (g) *******************************

{3075.0, 4100.0, nan, nan, 3600.0, 4625.0, 5650.0, 3100.0, 5150.0, 3625.0, 4650.0, 4150.0, 3650.0, 4675.0, 5700.0, 3150.0, 5200.0, 3675.0, 4700.0, 3175.0, 4200.0, 3700.0, 4725.0, 5750.0, 3200.0, 5250.0, 2700.0, 3725.0, 4750.0, 4250.0, 6300.0, 3750.0, 4775.0, 5800.0, 3250.0, 4275.0, 5300.0, 3775.0, 4800.0, 3275.0, 4300.0, 3800.0, 5850.0, 3300.0, 5350.0, 3825.0, 4850.0, 3325.0, 4350.0, 3850.0, 4875.0, 3350.0, 4375.0, 5400.0, 2850.0, 3875.0, 4900.0, 4400.0, 3900.0, 4925.0, 5950.0, 3400.0, 5450.0, 2900.0, 4950.0, 3425.0, 4450.0, 2925.0, 3950.0, 4975.0, 6000.0, 3450.0, 4475.0, 5500.0, 3975.0, 5000.0, 3475.0, 4500.0, 2975.0, 4000.0, 6050.0, 3500.0, 5550.0, 3000.0, 5050.0, 3525.0, 4550.0, 4050.0, 3550.0, 4575.0, 5600.0, 3050.0, 4075.0, 5100.0, 3575.0, 4600.0}
******************************* Sex *******************************

{'FEMALE', 'MALE', nan}
******************************* Delta 15 N (o/oo) *******************************

{7.88494, 8.93465, 9.98044, 8.46894, 8.90027, 8.47781, 8.19539, 8.67538, 8.94956, 8.36821, 8.76651, 9.18718, 10.02019, 8.94332, 8.8964, 8.63243, 8.35396, 8.01979, 9.4606, 9.27158, 9.07826, 8.79665, 8.9846, 9.46929, 9.62357, 9.65061, 8.70642, 8.2066, 8.15426, 8.29671, 8.30515, 8.14756, 8.38404, 8.33524, 8.32718, 9.42666, 9.49283, 9.22537, 9.45827, 9.29808, 8.39299, 7.90436, 8.65914, 8.30231, 7.99184, 8.75984, 8.65015, 8.51951, 8.08138, 8.74802, 9.25769, 9.22286, 9.21292, 8.87988, 9.14863, 10.02372, 9.11066, 9.07825, 8.63551, 8.27141, 8.96472, 8.96436, 8.24651, nan, 8.65803, 8.63259, 8.58063, 8.27376, nan, 8.01485, 8.2345, 8.23468, 8.86495, 8.83352, 8.27102, 7.76843, 7.69778, 7.993, 8.24694, 8.24515, 8.88098, 8.64701, 8.58487, 8.14567, 8.9399, 9.07878, 8.37615, 8.27595, nan, 8.29226, 8.12311, 8.41017, 8.74647, nan, 8.93997, 7.8962, 8.97025, 8.62623, 8.86853, 8.73762, 8.18658, 9.25177, 9.46819, 8.40327, 8.60447, 9.02657, 9.04296, 9.415, 8.45738, 7.79958, 8.24691, 8.59147, nan, 8.30166, nan, 7.8208, 7.96935, 8.81668, 8.68747, 8.47257, 8.49854, 8.47938, 9.56534, 9.00642, 9.32277, 9.59462, 9.05674, 9.36799, 9.06829, 8.04111, 7.90971, 7.96621, 7.89744, 8.03659, 8.5964, 8.79787, 8.79581, 8.04787, 9.22033, 9.15308, 9.05736, 9.43684, 8.48367, 9.32169, 8.36701, 8.62264, 8.25818, 8.45167, 8.36936, 8.85664, 8.41151, 7.63884, 8.13643, 8.19579, 8.24253, 8.19749, 8.52566, 8.47829, 9.43146, 9.18021, 9.08458, 9.36493, 9.39305, 9.43782, 8.58319, 8.3639, 8.85699, 9.2151, 8.39459, 8.94365, 7.92358, 7.97408, 8.56192, 8.77322, 8.08354, 7.9953, 7.84057, 8.55583, 8.43423, 8.57087, 8.97533, 8.83502, 9.41131, 9.23196, 9.01079, 9.32105, 8.1631, 9.2881, 9.59245, 9.26715, 9.70465, 8.15566, 8.16582, 8.20106, 8.33825, 8.48204, 8.717, 8.90002, 8.43951, 8.47067, 9.93727, 9.68933, 9.51929, 9.29078, 9.03935, 9.61734, 8.4207, 8.12691, 8.24246, 7.77672, 7.6322, 7.6887, nan, 8.90723, 8.98705, 8.35802, 8.41837, 8.80967, 8.10417, 9.19031, 9.37369, 9.49645, 9.63954, 9.69756, 9.4618, 9.74144, 8.19101, nan, 8.95063, nan, 8.39867, 8.56708, 8.57199, 8.26548, 7.83733, 7.63452, 7.82381, 7.8881, 8.30817, 8.2554, 8.6287, 8.68744, 8.20042, 8.55868, 9.53262, 9.6895, 9.46985, 8.63604, 8.47827, 9.72764, 9.66523, 8.84451, 9.8059, 9.77528, 8.48095, 8.3118, 8.71078, 8.10231, 8.46531, 8.77018, nan, nan, 8.32359, 8.66496, 8.14705, 8.47173, 8.65466, 8.92069, 9.13362, 9.35416, 9.74492, 9.18985, 9.51784, 9.11616, nan, nan, 9.36392, 9.75486, 8.2993, 8.51362, 7.88863, 7.68528, nan, 8.27428, 8.61651, 8.14776, 8.80186, 8.28601, 9.10702, 9.88809, 9.80589, 9.18528, 9.50772, 9.49106, 9.31735, 9.0233, 9.35138, 8.64931, 8.78557, 8.46616, 8.21634, 8.35078, 8.10277, 7.96491, 8.3554, 8.63701, 8.13746, 8.22673, 8.38289, 9.11006, 9.28153, 9.79532, 9.12277, 8.66271, 9.17847, 9.108, 10.02544, 9.63074, 9.14382, 8.91434, 8.60092, 8.03624, 8.72037, 8.50153, 8.88942, 8.53018, 8.11238, 9.37608, 9.34089, 9.30722, 8.07137, 8.95998, 9.36668, 9.2372, 9.04218, 9.02642, 8.63488, 8.49662, 8.38324, 9.23408, 8.48789, 8.85562, 8.49915, 8.56674}
******************************* Delta 13 C (o/oo) *******************************

{-26.06594, -25.03469, -26.33867, -26.18466, -26.18444, -26.20372, -26.79093, -26.57585, -26.2253, -26.39677, -25.95399, -25.62618, -25.89677, -25.8306, -25.33302, -24.65859, -25.69199, -24.45721, -25.39587, -26.83006, -24.80526, -26.06943, -26.75621, -24.69638, -24.43062, nan, -25.38157, -25.42826, -26.57563, -25.32829, -25.27385, -25.54456, -26.7489, -26.05621, -25.48025, -25.79203, -26.08165, -25.77951, -26.65931, -26.07021, -26.06967, -26.22664, -26.08547, nan, -26.68867, -25.22664, -26.86127, -26.20538, -26.78733, -26.03679, -26.3433, -25.29805, -26.01363, -26.32909, -26.59467, -26.65359, -26.22227, -25.23319, -25.5139, -25.09368, -26.79846, -25.73722, -24.61867, -24.805, -24.29229, -24.60882, -24.36088, -24.59467, -24.31198, -24.40753, -24.62717, -24.70615, -26.11657, -25.85203, nan, -26.41218, -25.77264, -25.95541, nan, -26.23886, nan, -25.80208, -26.18599, nan, -25.88547, nan, nan, nan, -26.05756, nan, -26.35425, -26.15531, -26.11244, -26.76821, -26.03495, -26.06209, -26.63085, -26.38085, -25.81513, -25.3933, -25.4276, -25.49448, -25.19837, -24.90816, -24.52896, -24.52698, -25.11223, -25.0602, -24.59996, -24.6844, -24.55644, -24.45195, -26.21019, nan, -26.84374, -26.13971, -26.27853, -26.70968, -25.32176, -26.42406, -26.57941, -25.54976, -25.6821, -24.4228, -26.44787, -25.14591, -26.1165, -26.38396, -25.97696, -26.19444, -25.52473, -26.84506, -25.01185, -26.7699, -26.01549, -26.84272, -26.5929, -26.84415, -26.37809, -26.30037, -25.21799, -25.57956, -26.30019, -25.37899, -25.37746, -24.26375, -24.59066, -24.6879, -24.34684, -24.25255, -24.89958, -26.24369, -25.06691, -26.63405, -26.13832, -25.46172, -25.39369, -26.09635, -24.53494, -26.2766, -25.79529, -26.17213, -26.60436, -24.66867, -24.48153, -25.89741, -24.78984, -26.06203, -25.89834, -24.10255, -26.18161, -26.22848, -26.09989, -26.61601, -26.55602, -26.46254, -26.0245, -26.79053, -25.50811, -26.55351, -25.36288, -26.23027, -24.69454, -24.7557, -24.49433, -24.41562, -25.04169, -24.48403, -24.64162, -24.54903, -26.60023, -26.18763, -25.40075, -26.07081, -26.69166, -25.23453, -25.46782, -25.46327, nan, -25.42621, -25.07683, -26.89644, -26.11046, -25.81012, nan, -25.11609, -26.15775, -26.12417, -26.11199, -26.86352, -26.67799, -25.39181, -25.45171, -26.61414, -25.83352, -25.38017, -24.23592, -24.57994, -24.56481, -24.73735, -25.50562, -24.66259, -26.51382, -23.90309, -23.78767, -24.84059, -25.49523, -25.86482, -26.24837, -25.48383, -25.76147, -26.02002, -25.88156, -26.86485, -25.46569, -26.04726, -26.72751, -25.21315, -26.28055, -25.13993, -25.03474, -25.29856, -25.23061, -25.22588, -26.60484, -26.21569, -26.32601, -26.66958, -26.54718, -25.79189, -25.4168, -26.45978, -25.0102, -25.18543, -24.404, -25.01745, -25.61039, -25.00169, -24.64335, -24.97134, -24.47142, -24.66188, -24.31912, -24.86594, -26.07821, -26.01152, -25.53768, -25.69195, -25.09383, -26.16524, -26.3146, -26.40943, -26.84166, -26.74809, -26.61075, -26.44815, -25.12255, -26.38092, -25.96013, -26.9547, -26.27573, -24.77227, -26.42018, -26.07188, -25.52627, -26.04117, -26.21651, -26.74249, -25.19017, -26.69543, -26.50086, -26.11969, -25.32426, -25.69327, -26.38986, -25.0639, -26.78958, -26.68311, -26.7765, -26.1396, -26.09294, -25.1455, -25.60826, -25.57647, -25.88798, -25.79549, -24.7294, -24.35575, -24.65786, -24.38933, -25.03492, -24.30229, -24.17282, -24.68741, -24.36202, -24.3613, -26.15003, -26.49288, -26.61788, -26.71199, -26.79358, -24.16566, -24.59897, -26.23613, -25.98843, -24.38751, -26.12989, -27.01854, -26.5387, -26.36678, -24.54704, -24.59513, -26.72791, -26.42563, -25.70711, -26.03442, -25.3947, -26.8154, -24.45189, -24.90024, -23.89017, -26.48973, -26.36863, -26.70783}
[112]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 12 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Species              344 non-null    object 
 1   Region               344 non-null    object 
 2   Island               344 non-null    object 
 3   Stage                344 non-null    object 
 4   Clutch Completion    344 non-null    object 
 5   Culmen Length (mm)   342 non-null    float64
 6   Culmen Depth (mm)    342 non-null    float64
 7   Flipper Length (mm)  342 non-null    float64
 8   Body Mass (g)        342 non-null    float64
 9   Sex                  334 non-null    object 
 10  Delta 15 N (o/oo)    330 non-null    float64
 11  Delta 13 C (o/oo)    331 non-null    float64
dtypes: float64(6), object(6)
memory usage: 32.4+ KB
[113]:
df.isnull().sum()
[113]:
Species                 0
Region                  0
Island                  0
Stage                   0
Clutch Completion       0
Culmen Length (mm)      2
Culmen Depth (mm)       2
Flipper Length (mm)     2
Body Mass (g)           2
Sex                    10
Delta 15 N (o/oo)      14
Delta 13 C (o/oo)      13
dtype: int64
[114]:
df.columns
[114]:
Index(['Species', 'Region', 'Island', 'Stage', 'Clutch Completion',
       'Culmen Length (mm)', 'Culmen Depth (mm)', 'Flipper Length (mm)',
       'Body Mass (g)', 'Sex', 'Delta 15 N (o/oo)', 'Delta 13 C (o/oo)'],
      dtype='object')
[115]:
#Numerical column --> 'Culmen Length (mm)', 'Culmen Depth (mm)', 'Flipper Length (mm)','Body Mass (g)', 'Delta 15 N (o/oo)', 'Delta 13 C (o/oo)'
#Char Variable --> 'Sex',
[116]:
df['Sex'].value_counts()
[116]:
Sex
MALE      169
FEMALE    165
Name: count, dtype: int64
[117]:
df['Sex'] = df['Sex'].fillna('MALE')
[118]:
#Splitting the data into train and test before doing Simple_Imputer
#median is not influenced by outlier
#so impute all 6 variable at one go rather than one by one
# x = df.iloc[:,1:]
# y= df[['Species']]
numerical = ['Culmen Length (mm)','Culmen Depth (mm)','Flipper Length (mm)','Body Mass (g)','Delta 15 N (o/oo)','Delta 13 C (o/oo)']
categorical = ['Species', 'Region', 'Island', 'Stage', 'Clutch Completion','Sex']
[119]:
y.head()
[119]:
Species
0 Adelie Penguin (Pygoscelis adeliae)
1 Adelie Penguin (Pygoscelis adeliae)
2 Adelie Penguin (Pygoscelis adeliae)
3 Adelie Penguin (Pygoscelis adeliae)
4 Adelie Penguin (Pygoscelis adeliae)
[120]:
Selection deleted
df['Species'].value_counts()
[120]:
Species
Adelie Penguin (Pygoscelis adeliae)          152
Gentoo penguin (Pygoscelis papua)            124
Chinstrap penguin (Pygoscelis antarctica)     68
Name: count, dtype: int64
[121]:
# df['Culmen Length (mm)'] = df['Culmen Length (mm)'].fillna(df['Culmen Length (mm)'].median())
# df['Culmen Depth (mm)'] = df['Culmen Depth (mm)'].fillna(df['Culmen Depth (mm)'].median())
# df['Flipper Length (mm)'] = df['Flipper Length (mm)'].fillna(df['Flipper Length (mm)'].median())
# df['Body Mass (g)'] = df['Body Mass (g)'].fillna(df['Body Mass (g)'].median())
# df['Delta 15 N (o/oo)'] = df['Delta 15 N (o/oo)'].fillna(df['Delta 15 N (o/oo)'].median())
# df['Delta 13 C (o/oo)'] = df['Delta 13 C (o/oo)'].fillna(df['Delta 13 C (o/oo)'].median())
[122]:
Selection deleted
from sklearn.impute import SimpleImputer
impute = SimpleImputer(missing_values = np.nan , strategy='median')
df[numerical] = impute.fit_transform(df[numerical])

"""
Char imputation by mode concept
impute = SimpleImputer(missing_values = np.nan , strategy='most_frequent')
df[categorical] = impute.fit_transform(df[categorical])
"""

[122]:
"\nChar imputation by mode concept\nimpute = SimpleImputer(missing_values = np.nan , strategy='most_frequent')\ndf[categorical] = impute.fit_transform(df[categorical])\n"
[123]:
Selection deleted
df.isnull().sum()
[123]:
Species                0
Region                 0
Island                 0
Stage                  0
Clutch Completion      0
Culmen Length (mm)     0
Culmen Depth (mm)      0
Flipper Length (mm)    0
Body Mass (g)          0
Sex                    0
Delta 15 N (o/oo)      0
Delta 13 C (o/oo)      0
dtype: int64
[124]:
#Preprocessing part 2
df.head()
[124]:
Species Region Island Stage Clutch Completion Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo)
0 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage Yes 39.10 18.7 181.0 3800.0 MALE 8.652405 -25.83352
1 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage Yes 39.50 17.4 186.0 3800.0 FEMALE 8.949560 -24.69454
2 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage Yes 40.30 18.0 190.0 3250.0 FEMALE 8.368210 -25.33302
3 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage Yes 44.45 17.3 197.0 4050.0 MALE 8.652405 -25.83352
4 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage Yes 36.70 19.3 193.0 3450.0 FEMALE 8.766510 -25.32426
# Encoding

Encoding¶

[125]:
# Decision Tree I need to go with Label Encoder
# Linear Regression I need to go with OHE
[126]:
# df['Species'] =
[127]:
Selection deleted
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
df['Species'] = encoder.fit_transform(df['Species'])
df['Region'] = encoder.fit_transform(df['Region'])
df['Island'] = encoder.fit_transform(df['Island'])
df['Stage'] = encoder.fit_transform(df['Stage'])
df['Clutch Completion'] = encoder.fit_transform(df['Clutch Completion'])
df['Sex'] = encoder.fit_transform(df['Sex'])
[128]:
df.head()
[128]:
Species Region Island Stage Clutch Completion Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo)
0 0 0 2 0 1 39.10 18.7 181.0 3800.0 1 8.652405 -25.83352
1 0 0 2 0 1 39.50 17.4 186.0 3800.0 0 8.949560 -24.69454
2 0 0 2 0 1 40.30 18.0 190.0 3250.0 0 8.368210 -25.33302
3 0 0 2 0 1 44.45 17.3 197.0 4050.0 1 8.652405 -25.83352
4 0 0 2 0 1 36.70 19.3 193.0 3450.0 0 8.766510 -25.32426
[129]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 12 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Species              344 non-null    int32  
 1   Region               344 non-null    int32  
 2   Island               344 non-null    int32  
 3   Stage                344 non-null    int32  
 4   Clutch Completion    344 non-null    int32  
 5   Culmen Length (mm)   344 non-null    float64
 6   Culmen Depth (mm)    344 non-null    float64
 7   Flipper Length (mm)  344 non-null    float64
 8   Body Mass (g)        344 non-null    float64
 9   Sex                  344 non-null    int32  
 10  Delta 15 N (o/oo)    344 non-null    float64
 11  Delta 13 C (o/oo)    344 non-null    float64
dtypes: float64(6), int32(6)
memory usage: 24.3 KB
# Outlier

Outlier¶

[130]:
#tree grow larger then you have to handle it
#or you can determing by feature importance part
[131]:
df.describe()
[131]:
Species Region Island Stage Clutch Completion Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo)
count 344.000000 344.0 344.000000 344.0 344.000000 344.000000 344.000000 344.000000 344.000000 344.000000 344.000000 344.000000
mean 0.918605 0.0 0.662791 0.0 0.895349 43.925000 17.152035 200.877907 4201.017442 0.520349 8.730086 -25.691855
std 0.893320 0.0 0.726194 0.0 0.306549 5.443792 1.969060 14.032539 799.618885 0.500313 0.540630 0.779277
min 0.000000 0.0 0.000000 0.0 0.000000 32.100000 13.100000 172.000000 2700.000000 0.000000 7.632200 -27.018540
25% 0.000000 0.0 0.000000 0.0 1.000000 39.275000 15.600000 190.000000 3550.000000 0.000000 8.307415 -26.285460
50% 1.000000 0.0 1.000000 0.0 1.000000 44.450000 17.300000 197.000000 4050.000000 1.000000 8.652405 -25.833520
75% 2.000000 0.0 1.000000 0.0 1.000000 48.500000 18.700000 213.000000 4750.000000 1.000000 9.136170 -25.089467
max 2.000000 0.0 2.000000 0.0 1.000000 59.600000 21.500000 231.000000 6300.000000 1.000000 10.025440 -23.787670
# Feature Scaling
#because I am not doing any differentiation in DT

Feature Scaling¶

#because I am not doing any differentiation in DT

[132]:
import dtale
dtale.show(df)
[132]:
# Imbalance Treatment
Imbalance Treatment¶


# Multivariate Analysis
# To understand if Else condition of DT
Multivariate Analysis¶


To understand if Else condition of DT¶


[137]:
sns.scatterplot(x='Culmen Length (mm)',y='Culmen Depth (mm)',data=df,hue='Species')
[137]:
<Axes: xlabel='Culmen Length (mm)', ylabel='Culmen Depth (mm)'>
#![image.png](attachment:e9966aff-0f94-458b-b55d-6dc4ec1478e4.png)
#image.png


[138]:
sns.pairplot(data=df,hue='Species')
plt.show()
[142]:
plt.figure(figsize=(15,8))
sns.heatmap(df.corr(),annot=True,cmap='coolwarm')
plt.show()
[ ]:
# I am getting 0.85 in heatmap but in DT we dont take care of multicollinearity,so you can ignore this

[143]:
sns.catplot(data=df,x='Species',y='Culmen Length (mm)',kind='box',col='Sex')
2024-09-25 09:23:42,441 - INFO     - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2024-09-25 09:23:42,448 - INFO     - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.

[143]:
<seaborn.axisgrid.FacetGrid at 0x285db733b90>
[ ]:
# Split the data into independent and dependent variable
[147]:
x = df.iloc[:,1:]
y=df.iloc[:,0]
[148]:
x.head()
[148]:




  
    

      
      Region
      Island
      Stage
      Clutch Completion
      Culmen Length (mm)
      Culmen Depth (mm)
      Flipper Length (mm)
      Body Mass (g)
      Sex
      Delta 15 N (o/oo)
      Delta 13 C (o/oo)
    

  
  
    

      0
      0
      2
      0
      1
      39.10
      18.7
      181.0
      3800.0
      1
      8.652405
      -25.83352
    

    

      1
      0
      2
      0
      1
      39.50
      17.4
      186.0
      3800.0
      0
      8.949560
      -24.69454
    

    

      2
      0
      2
      0
      1
      40.30
      18.0
      190.0
      3250.0
      0
      8.368210
      -25.33302
    

    

      3
      0
      2
      0
      1
      44.45
      17.3
      197.0
      4050.0
      1
      8.652405
      -25.83352
    

    

      4
      0
      2
      0
      1
      36.70
      19.3
      193.0
      3450.0
      0
      8.766510
      -25.32426
    

  



[149]:
y.head()
[149]:
0    0
1    0
2    0
3    0
4    0
Name: Species, dtype: int32
# Imbalance Treatment
Imbalance Treatment¶


# Do UnderSampling in Text Analytics where we have huge dataset
# Always do oversampling
Do UnderSampling in Text Analytics where we have huge dataset¶


Always do oversampling¶


[151]:
Selection deleted
import imblearn
from imblearn.over_sampling import RandomOverSampler
ros= RandomOverSampler()
x_over,y_over = ros.fit_resample(x,y)
print("Imbalance Datapoints:",y.value_counts())
print()
print("Balance Datapoints:",y_over.value_counts())
Imbalance Datapoints: Species
0    152
2    124
1     68
Name: count, dtype: int64

Balance Datapoints: Species
0    152
1    152
2    152
Name: count, dtype: int64

[152]:
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x_over,y_over,random_state=1,test_size=0.25,stratify=y_over)

# Building Decision Tree Model
Building Decision Tree Model¶


[166]:
Selection deleted
from sklearn.tree import DecisionTreeClassifier

# Approach 1 - Gini
dtree1 = DecisionTreeClassifier(criterion='gini')#by default gini provided
dtree1.fit(x_train,y_train)

# Approach 1 - Entropy
dtree2 = DecisionTreeClassifier(criterion='entropy')
dtree2.fit(x_train,y_train)

[166]:
DecisionTreeClassifier(criterion='entropy')
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DecisionTreeClassifier(criterion='entropy')
 
# Predict the test data by using both the models
Predict the test data by using both the models¶


[167]:
y_pred_dt1_train = dtree1.predict(x_train)
y_pred_dt1_test = dtree1.predict(x_test)

y_pred_dt2_train = dtree2.predict(x_train)
y_pred_dt2_test = dtree2.predict(x_test)
# Evaluate Your Model
Evaluate Your Model¶


[168]:
from sklearn.metrics import classification_report ,confusion_matrix, accuracy_score
[169]:
Selection deleted
print(classification_report(y_train,y_pred_dt1_train))
print()
print(classification_report(y_test,y_pred_dt1_test))
              precision    recall  f1-score   support

           0       1.00      1.00      1.00       114
           1       1.00      1.00      1.00       114
           2       1.00      1.00      1.00       114

    accuracy                           1.00       342
   macro avg       1.00      1.00      1.00       342
weighted avg       1.00      1.00      1.00       342


              precision    recall  f1-score   support

           0       0.97      0.97      0.97        38
           1       1.00      0.97      0.99        38
           2       0.95      0.97      0.96        38

    accuracy                           0.97       114
   macro avg       0.97      0.97      0.97       114
weighted avg       0.97      0.97      0.97       114


[170]:
print(classification_report(y_train,y_pred_dt2_train))
print()
print(classification_report(y_test,y_pred_dt2_test))
              precision    recall  f1-score   support

           0       1.00      1.00      1.00       114
           1       1.00      1.00      1.00       114
           2       1.00      1.00      1.00       114

    accuracy                           1.00       342
   macro avg       1.00      1.00      1.00       342
weighted avg       1.00      1.00      1.00       342


              precision    recall  f1-score   support

           0       1.00      0.95      0.97        38
           1       0.97      1.00      0.99        38
           2       0.97      1.00      0.99        38

    accuracy                           0.98       114
   macro avg       0.98      0.98      0.98       114
weighted avg       0.98      0.98      0.98       114


[171]:
print(confusion_matrix(y_train,y_pred_dt1_train))
print()
print(confusion_matrix(y_test,y_pred_dt1_test))
[[114   0   0]
 [  0 114   0]
 [  0   0 114]]

[[37  0  1]
 [ 0 37  1]
 [ 1  0 37]]

[172]:
print(confusion_matrix(y_train,y_pred_dt2_train))
print()
print(confusion_matrix(y_test,y_pred_dt2_test))
[[114   0   0]
 [  0 114   0]
 [  0   0 114]]

[[36  1  1]
 [ 0 38  0]
 [ 0  0 38]]

[176]:
print("Train Accuracy - Gini: ",accuracy_score(y_train,y_pred_dt1_train))
print()
print("Test Accuracy - Gini: ",accuracy_score(y_test,y_pred_dt1_test))
Train Accuracy - Gini:  1.0

Test Accuracy - Gini:  0.9736842105263158

[178]:
print("Train Accuracy -Entropy :",accuracy_score(y_train,y_pred_dt2_train))
print()
print("Test Accuracy -Entropy :",accuracy_score(y_test,y_pred_dt2_test))
Train Accuracy -Entropy : 1.0

Test Accuracy -Entropy : 0.9824561403508771

# Cross Validation - K_Fold_Method
Cross Validation - K_Fold_Method¶


[179]:
from sklearn.model_selection import cross_val_score
accuracy =  cross_val_score(dtree1,x_train,y_train,cv=10)
[180]:
print(accuracy)
print()
print(accuracy.mean())
print()
print(accuracy.max())
[0.88571429 1.         1.         1.         0.97058824 0.94117647
 1.         0.94117647 0.97058824 1.        ]

0.9709243697478991

1.0

[187]:
Selection deleted
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.metrics import confusion_matrix

# Compute the confusion matrix
cm = confusion_matrix(y_test, dtree1.predict(x_test))

# Display the confusion matrix
disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot()

plt.show()
# Post Pruning
Post Pruning¶


[189]:
Selection deleted

dtree1.feature_importances_
[189]:
array([0.        , 0.01723807, 0.        , 0.        , 0.04058196,
       0.        , 0.46088301, 0.00556215, 0.        , 0.        ,
       0.47573481])
[190]:
pd.DataFrame(index=x.columns,data=dtree1.feature_importances_,columns = ["Feature Importance"])
[190]:




  
    

      
      Feature Importance
    

  
  
    

      Region
      0.000000
    

    

      Island
      0.017238
    

    

      Stage
      0.000000
    

    

      Clutch Completion
      0.000000
    

    

      Culmen Length (mm)
      0.040582
    

    

      Culmen Depth (mm)
      0.000000
    

    

      Flipper Length (mm)
      0.460883
    

    

      Body Mass (g)
      0.005562
    

    

      Sex
      0.000000
    

    

      Delta 15 N (o/oo)
      0.000000
    

    

      Delta 13 C (o/oo)
      0.475735
    

  



[ ]:
Order of feature Importance for variables
#Delta 13 C (o/oo)	0.475735
#Flipper Length (mm)	0.460883
[191]:
from sklearn.tree import plot_tree
plt.figure(figsize=(12,8))
plot_tree(dtree1)
plt.show()
[193]:
from sklearn.tree import plot_tree
plt.figure(figsize=(12,8))
plot_tree(dtree1,filled=True,feature_names=x.columns)
plt.show()
#values is dependent variable - species
#sample - how many data is available there

# Using HypeParamete in DT to apply post pruning Method

Using HypeParamete in DT to apply post pruning Method¶


[201]:
#where feature importnacevalue is not zero
prunned_tree = DecisionTreeClassifier(criterion='gini',max_depth=5)
prunned_tree.fit(x_train,y_train)
[201]:
DecisionTreeClassifier(max_depth=5)
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DecisionTreeClassifier(max_depth=5)
 
# User Definded Functions
User Definded Functions¶


[202]:
def report_model(model):
    model_preds = model.predict(x_test)
    print(classification_report(y_test,model_preds))
    print("\n")
    print(accuracy_score(y_test,model_preds))
    print("\n")
    plt.figure(figsize=(12,8),dpi=150)
    plot_tree(model,filled=True,feature_names = x.columns)
[203]:
report_model(prunned_tree)
#precision - macro avg       0.97      0.96      0.97       114
#Accuracy - 0.9649122807017544
              precision    recall  f1-score   support

           0       0.97      0.95      0.96        38
           1       1.00      0.97      0.99        38
           2       0.93      0.97      0.95        38

    accuracy                           0.96       114
   macro avg       0.97      0.96      0.97       114
weighted avg       0.97      0.96      0.97       114



0.9649122807017544



# Logistic Regression 
Logistic Regression¶


[205]:
Selection deleted
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression()
logit.fit(x_train,y_train)
[205]:
LogisticRegression()
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LogisticRegression()
 
[207]:
y_pred_train_logit = logit.predict(x_train)
y_pred_test_logit = logit.predict(x_test)
[213]:
print("Train Accuracy - Logit :", accuracy_score(y_train, y_pred_train_logit))
print()  # This will print a blank line
print("Test Accuracy - Logit :", accuracy_score(y_test, y_pred_test_logit))
Train Accuracy - Logit : 0.9970760233918129

Test Accuracy - Logit : 0.9912280701754386

[ ]:
# Overfitting situation
[ ]:
# For multiclass - don't go by Logistic Regression only go by DT or you can go by Random Forest (SOTA)
# Bagging Classifier
Bagging Classifier¶


[215]:
from sklearn.ensemble import BaggingClassifier
bagging = BaggingClassifier() # base_estimator = 'DT' by default
bagging.fit(x_train,y_train)
[215]:
BaggingClassifier()
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BaggingClassifier()
 
[217]:
y_pred_bagging_train = bagging.predict(x_train)
print()
y_pred_bagging_test = bagging.predict(x_test)
[218]:
print("Train Accuracy - Logit :", accuracy_score(y_train, y_pred_bagging_train))
print()  # This will print a blank line
print("Test Accuracy - Logit :", accuracy_score(y_test, y_pred_bagging_test))
Train Accuracy - Logit : 1.0

Test Accuracy - Logit : 0.9736842105263158

# Random Forest Classifier
Random Forest Classifier¶


[225]:
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(criterion='entropy',n_estimators=200,bootstrap=True,oob_score=True) #no concept of base estimator #hyperparameter tuning I am building here
rf.fit(x_train,y_train)
[225]:
RandomForestClassifier(criterion='entropy', n_estimators=200, oob_score=True)
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RandomForestClassifier(criterion='entropy', n_estimators=200, oob_score=True)
 
[226]:
Selection deleted
y_pred_rf_train = rf.predict(x_train)
print()
y_pred_rf_test = rf.predict(x_test)
[227]:
print("Train Accuracy - Logit :", accuracy_score(y_train, y_pred_rf_train))
print()  # This will print a blank line
print("Test Accuracy - Logit :", accuracy_score(y_test, y_pred_rf_test))
Train Accuracy - Logit : 1.0

Test Accuracy - Logit : 0.9912280701754386

# Applying Cross Validation Method
Applying Cross Validation Method¶


[228]:
Selection deleted
from sklearn.model_selection import cross_val_score
Training_Accuracy = cross_val_score(rf,x_train,y_train,cv=10)
print(Training_Accuracy)
[0.97142857 0.97142857 1.         1.         0.97058824 0.97058824
 1.         0.97058824 1.         1.        ]

[229]:
Selection deleted
print(Training_Accuracy.mean())
print()
print(Training_Accuracy.max())
0.9854621848739497

1.0

[ ]:

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